
@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV	 = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
@String(ICLR  = {ICLR})
@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})

@misc{Mindspore,
  title        = {Mindspore},
  howpublished = {\url{https://www.mindspore.cn/}}
}

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  pages     = {126--135},
  year      = {2017}
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@article{bengio2013representation,
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  publisher = {BMVA press}
}

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  title   = {Once-for-all: Train one network and specialize it for efficient deployment},
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  year    = {2019}
}

@article{chen2018neural,
  title   = {Neural ordinary differential equations},
  author  = {Chen, Ricky TQ and Rubanova, Yulia and Bettencourt, Jesse and Duvenaud, David K},
  journal = {Advances in neural information processing systems},
  volume  = {31},
  year    = {2018}
}

@article{croes1958method,
  title     = {A method for solving traveling-salesman problems},
  author    = {Croes, Georges A},
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  volume    = {6},
  number    = {6},
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  publisher = {INFORMS}
}

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  year      = {2018},
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  journal   = {IEEE transactions on pattern analysis and machine intelligence},
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  publisher = {IEEE}
}


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@inproceedings{huang2017densely,
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  year      = {2017}
}


@book{hughes2020calculus,
  title     = {Calculus: Single and multivariable},
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  year      = {2020},
  publisher = {John Wiley \& Sons}
}


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  title     = {Learning multiple layers of features from tiny images},
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  year      = {2009},
  publisher = {Toronto, ON, Canada}
}


@article{krizhevsky2012imagenet,
  title   = {Imagenet classification with deep convolutional neural networks},
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  volume  = {25},
  year    = {2012}
}

@article{kuurkova1992kolmogorov,
  title     = {Kolmogorov's theorem and multilayer neural networks},
  author    = {K{\uu}rkov{\'a}, V{\v{e}}ra},
  journal   = {Neural networks},
  volume    = {5},
  number    = {3},
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  publisher = {Elsevier}
}

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  title   = {The traveling salesman problem: a guided tour of combinatorial optimization},
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  year    = {1985}
}


@book{leader2022numerical,
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}


@article{lee2018snip,
  title   = {Snip: Single-shot network pruning based on connection sensitivity},
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  year    = {2018}
}

@article{li2016pruning,
  title   = {Pruning filters for efficient convnets},
  author  = {Li, Hao and Kadav, Asim and Durdanovic, Igor and Samet, Hanan and Graf, Hans Peter},
  journal = {arXiv preprint arXiv:1608.08710},
  year    = {2016}
}

@inproceedings{lim2017enhanced,
  title     = {Enhanced deep residual networks for single image super-resolution},
  author    = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Mu Lee, Kyoung},
  booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
  pages     = {136--144},
  year      = {2017}
}

@article{liu2020deep,
  title     = {Deep learning for generic object detection: A survey},
  author    = {Liu, Li and Ouyang, Wanli and Wang, Xiaogang and Fieguth, Paul and Chen, Jie and Liu, Xinwang and Pietik{\"a}inen, Matti},
  journal   = {International journal of computer vision},
  volume    = {128},
  pages     = {261--318},
  year      = {2020},
  publisher = {Springer}
}

@inproceedings{martin2001database,
  title        = {A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics},
  author       = {Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
  booktitle    = {Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001},
  volume       = {2},
  pages        = {416--423},
  year         = {2001},
  organization = {IEEE}
}

@inproceedings{molchanov2019importance,
  title     = {Importance estimation for neural network pruning},
  author    = {Molchanov, Pavlo and Mallya, Arun and Tyree, Stephen and Frosio, Iuri and Kautz, Jan},
  booktitle = {Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages     = {11264--11272},
  year      = {2019}
}


@article{molchanov2016pruning,
  title   = {Pruning convolutional neural networks for resource efficient inference},
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  journal = {arXiv preprint arXiv:1611.06440},
  year    = {2016}
}


@article{li2020neural,
  title   = {Neural operator: Graph kernel network for partial differential equations},
  author  = {Li, Zongyi and Kovachki, Nikola and Azizzadenesheli, Kamyar and Liu, Burigede and Bhattacharya, Kaushik and Stuart, Andrew and Anandkumar, Anima},
  journal = {arXiv preprint arXiv:2003.03485},
  year    = {2020}
}


@article{paszke2019pytorch,
  title   = {Pytorch: An imperative style, high-performance deep learning library},
  author  = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and others},
  journal = {Advances in neural information processing systems},
  volume  = {32},
  year    = {2019}
}

@inproceedings{peng2019collaborative,
  title        = {Collaborative channel pruning for deep networks},
  author       = {Peng, Hanyu and Wu, Jiaxiang and Chen, Shifeng and Huang, Junzhou},
  booktitle    = {International conference on machine learning},
  pages        = {5113--5122},
  year         = {2019},
  organization = {PMLR}
}

@article{purwins2019deep,
  title     = {Deep learning for audio signal processing},
  author    = {Purwins, Hendrik and Li, Bo and Virtanen, Tuomas and Schl{\"u}ter, Jan and Chang, Shuo-Yiin and Sainath, Tara},
  journal   = {IEEE Journal of Selected Topics in Signal Processing},
  volume    = {13},
  number    = {2},
  pages     = {206--219},
  year      = {2019},
  publisher = {IEEE}
}


@article{russakovsky2015imagenet,
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  journal   = {International journal of computer vision},
  volume    = {115},
  pages     = {211--252},
  year      = {2015},
  publisher = {Springer}
}

@article{scarselli1998universal,
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  number    = {1},
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@article{shocher2020discrete,
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  year    = {2020}
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@inproceedings{wang2018deep,
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}

@article{wang2020deep,
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@article{wen2016learning,
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  year    = {2016}
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@inproceedings{wu2019fbnet,
  title     = {Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search},
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}

@inproceedings{yang2019quantization,
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@inproceedings{yang2008image,
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@inproceedings{yang2020cars,
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@inproceedings{zeyde2012single,
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@inproceedings{zhang2018residual,
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@inproceedings{zhuang2020training,
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  pages     = {1488--1497},
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}


